code: def test_group_by_length_with_dict(self):
      # Get some inputs of random lengths
      data = []
      for _ in range(6):
          input_ids = torch.randint(0, 25, (100,)).tolist()
          data.append({"input_ids": input_ids})
      # Put one bigger than the others to check it ends up in first position
      data[3]["input_ids"] = torch.randint(0, 25, (105,)).tolist()
  
      indices = list(LengthGroupedSampler(4, dataset=data))
      # The biggest element should be first
      self.assertEqual(len(data[indices[0]]["input_ids"]), 105)
      # The indices should be a permutation of range(6)
      self.assertEqual(sorted(indices), list(range(6)))
  
imports: import copy
  import unittest
  import numpy
  from transformers.data.data_collator import default_data_collator
  from transformers.testing_utils import require_accelerate, require_torch
  from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
  from transformers.utils import is_torch_available
label_desc: Use image node_id 5 for calling the TrainerUtilsTest obj's underlying member method code with example usage: obj.test_group_by_length_with_dict() without return types